Developing Surrogate Models via Computer Based Experiments

Thombre, Mandar; Preisig, Heinz A.; Addis, Misganaw B.

doi:10.1016/b978-0-444-63578-5.50102-x

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B Surrogate Modelling1

Introduction1

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2017

2024

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Article6

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Cited by 15 publications

(2 citation statements)

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“…The error term ε is thus only due to lack-offit, and does not in general have a specific distribution. This means that classical test statistics testing the significance of ε may be inapplicable [19]. Additionally, the classical designs are typically spread out towards the extremes in design space -leaving holes in the experiment design in the region of most interest.…”

Section: B Surrogate Modellingmentioning

confidence: 99%

Surrogate models for design and study of underground mine ventilation

Åstrand

Saarinen

Tavallaey

2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Section: B Surrogate Modellingmentioning

confidence: 99%

Surrogate models for design and study of underground mine ventilation

Åstrand

Saarinen

Tavallaey

2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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“…Moreover, for real flight data, it is difficult to find that which encompasses specific flight profiles necessary for system identification. Synthetic flight test data from online repositories [20,21], simulated flight data [10,22] and surrogate models [23,24] provide good alternatives.…”

Section: Introductionmentioning

confidence: 99%

Robust Aerodynamic Parameter Estimation of Unmanned Aircraft Based on Two-step Identification

Kimathi

Lantos

2023

Period. Polytech. Elec. Eng. Comp. Sci.

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This paper presents the estimation of stability and control derivatives of an unmanned aircraft. The aerodynamics are described using regressors composed of velocity, angular rates, flow angles and control surface deflections. The flight data is generated from numerical simulation of postulated equations of motion describing the aerodynamics model. Least squares based on the equation error method is used to estimate the parameters representing the different force and moment aerodynamic coefficients. Statistical analysis is done on the estimates to determine the accuracy and adequacy of the estimates to describe the aerodynamic model. A dynamic simulation based on the identified aerodynamic model is used to improve the parameter estimates through regression of the errors between the flight data and the model response. The aircraft under consideration is a scaled Yak-54 fixed wing unmanned aerial vehicle.

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